Systems and methods for utilizing unused network capacity for prefetch requests

ABSTRACT

Methods, systems, and computer-readable media are disclosed for utilizing unused network capacity for prefetch requests. One method includes: receiving, over a network, network traffic information from a network provider of the network; determining a threshold value for prefetch request fulfillment based on the received network traffic information; receiving, over the network, a plurality of prefetch requests from an application running on a mobile device connected to the network of the network provider; determining, for each prefetch request of the plurality of prefetch requests, a score for the prefetch request based on the received plurality of prefetch requests; and responding to, for each prefetch request of the plurality of prefetch requests, the prefetch request based on the determined threshold value and the determined score for the prefetch request.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This patent application is a continuation of and claims the benefit ofpriority to U.S. application Ser. No. 16/239,220, filed Jan. 3, 2019,which is a continuation of and claims the benefit of priority to U.S.application Ser. No. 15/196,659, filed on Jun. 29, 2016, now U.S. Pat.No. 10,218,811, issued Feb. 26, 2019, the entireties of which areincorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates to enabling service providers to utilizeunused network capacity that would otherwise be wasted. Moreparticularly, the present disclosure relates to utilizing unused networkcapacity for prefetching requests.

BACKGROUND

Mobile wireless communication networks have finite resources which aretypically shared among multiple mobile devices that access differentservices. Such services may include, for example, video streaming and/orinteractive messaging, e-mail, text messaging, web surfing, etc.Applications using different services can place varied demands on thewireless communication network. To address these demands, a network'scapacity is often larger than the amount used at any given time, inorder to provide an acceptable quality of experience for all of theusers and their respective applications.

However, there continues to be an increasing demand on mobile wirelesscommunication networks because of the increasing proliferation of mobiledevices and increasing use of applications and services (e.g., video andmusic services). Mobile broadband (i.e., network access via cellulartelephone tower and/or satellite link), in particular, has becomeoverburdened, especially during certain times of the day. Because theavailable mobile broadband spectrum is non-renewable and limited, thereis an increased motivation to make more efficient use of the fullcapacity of a network during peak use times and non-peak use times.

Thus, embodiments of the current disclosure relate to improvingutilization of unused network capacity during peak use times andnon-peak use times, and, more particularly, to utilization of unusednetwork capacity for fulfilling prefetch requests.

SUMMARY OF THE DISCLOSURE

Embodiments of the present disclosure include systems, methods, andcomputer-readable media for utilizing unused network capacity forprefetch requests.

According to embodiments of the present disclosure, computer-implementedmethods are disclosed for utilizing unused network capacity for prefetchrequests. One method includes: receiving, over a network at one or moreservers, network traffic information from a network provider of thenetwork; determining, by the one or more servers, a threshold value forprefetch request fulfillment based on the received network trafficinformation; receiving, over the network at the one or more servers, aplurality of prefetch requests from an application running on a mobiledevice connected to the network of the network provider; determining,for each prefetch request of the plurality of prefetch requests by theone or more servers, a score for the prefetch request based on thereceived plurality of prefetch requests; and responding to, for eachprefetch request of the plurality of prefetch requests by the one ormore servers, the prefetch request based on the determined thresholdvalue and the determined score for the prefetch request.

According to embodiments of the present disclosure, systems aredisclosed for utilizing unused network capacity for prefetch requests.One system includes a data storage device that stores instructionssystem for utilizing unused network capacity for prefetch requests; anda processor configured to execute the instructions to perform a methodincluding: receiving, over a network, network traffic information from anetwork provider of the network; determining a threshold value forprefetch request fulfillment based on the received network trafficinformation; receiving, over the network, a plurality of prefetchrequests from an application running on a mobile device connected to thenetwork of the network provider; determining, for each prefetch requestof the plurality of prefetch requests, a score for the prefetch requestbased on the received plurality of prefetch requests; and responding to,for each prefetch request of the plurality of prefetch requests, theprefetch request based on the determined threshold value and thedetermined score for the prefetch request.

According to embodiments of the present disclosure, non-transitorycomputer-readable media storing instructions that, when executed by acomputer, cause the computer to perform a method for utilizing unusednetwork capacity for prefetch requests are also disclosed. One method ofthe non-transitory computer-readable medium including: receiving, over anetwork, network traffic information from a network provider of thenetwork; determining a threshold value for prefetch request fulfillmentbased on the received network traffic information; receiving, over thenetwork, a plurality of prefetch requests from an application running ona mobile device connected to the network of the network provider;determining, for each prefetch request of the plurality of prefetchrequests, a score for the prefetch request based on the receivedplurality of prefetch requests; and responding to, for each prefetchrequest of the plurality of prefetch requests, the prefetch requestbased on the determined threshold value and the determined score for theprefetch request.

Additional objects and advantages of the disclosed embodiments will beset forth in part in the description that follows, and in part will beapparent from the description, or may be learned by practice of thedisclosed embodiments. The objects and advantages of the disclosedembodiments will be realized and attained by means of the elements andcombinations particularly pointed out in the appended claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the scope of disclosed embodiments, as setforth by the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various exemplary embodiments andtogether with the description, serve to explain the principles of thedisclosed embodiments.

FIG. 1 depicts a schematic diagram of a network environment for a methodof utilizing unused network capacity for prefetch requests, according toembodiments of the present disclosure;

FIG. 2 depicts a block diagram of a predictive prefetch server utilizinga plurality of sources to build a prefetch model for a user of a mobiledevice, according to embodiments of the present disclosure;

FIG. 3 depicts a block diagram of a method for utilizing unused networkcapacity for prefetch requests, according to embodiments of the presentdisclosure;

FIG. 4 depicts a block diagram of another method for utilizing unusednetwork capacity for prefetch requests, according to embodiments of thepresent disclosure;

FIG. 5 depicts a block diagram of a method for building a predictivemodel for prefetch requests, according to embodiments of the presentdisclosure; and

FIG. 6 depicts a block diagram of a method for utilizing unused networkcapacity for fulfilling prefetch request using a predictive model,according to embodiments of the present disclosure; and

FIG. 7 is a simplified functional block diagram of a computer configuredas a device for executing the methods of FIGS. 3-6, according toexemplary embodiments of the present disclosure.

It is to be understood that the figures are not necessarily drawn toscale, nor are the objects in the figures necessarily drawn to scale inrelationship to one another. The figures are depictions that areintended to bring clarity and understanding to various embodiments ofapparatuses, systems, and methods disclosed herein. Wherever possible,the same reference numbers will be used throughout the drawings to referto the same or like parts. Moreover, it should be appreciated that thedrawings are not intended to limit the scope of the present teachings inany way.

DETAILED DESCRIPTION OF EMBODIMENTS

The following detailed description refers to the accompanying drawings.The same reference numbers in different drawings may identify the sameor similar elements.

Systems and methods described herein enable wireless service providersto identify, offer, and monetize unused network capacity that wouldotherwise be wasted. The systems and methods may allow for unusedcapacity to be used for prefetching requests from applications runningon mobile devices, and may be used to determine which prefetch requeststo fulfill. In a conventional system, a wireless service provider mayallow network capacity to remain unused when not at a peak use time.Also, in a conventional system, applications and/or applicationproviders are unable to utilize the unused network capacity. Forexample, an application that uses prefetch requests may incur networkusage charges for a user of the application whether the user eventuallyrequests the content of the prefetch requests or not. Embodimentsdescribed herein may allow applications and application providers toutilize unused network capacity for prefetch requests, and may allow for“charging” the user for the data associated with the prefetch requestsonly if the user actually requests the content of the prefetch request.

According to one implementation, a predictive prefetch server mayreceive wireless network utilization information from a wireless networkprovider and may receive prefetch requests from mobile devices of usersthat utilize the wireless network of the wireless network provider. Uponreceiving the wireless network utilization information, the predictiveprefetch server may determine a threshold value based on the wirelessnetwork utilization information for the wireless network. The predictiveprefetch server may also examine, score, and rank each prefetch requestreceived from the mobile device. Based on the determined threshold valueand the ranking of the prefetch request, the predictive prefetch servermay determine which prefetch request to fulfill.

Additionally, the predictive prefetch server may also receive contentrequests from a user of the mobile device. The content requests mayinclude requests made by the user in a non-prefetch capacity. Thecontent requests and prefetch requests may be used to build a prefetchmodel, which may be used to more accurately score and rank prefetchrequest and may be used to detect for fraudulent prefect requests.

FIG. 1 depicts a diagram of an exemplary electronic network environment100 for utilizing unused wireless network capacity for prefetchingrequests, according to embodiments of the present disclosure. As shownin FIG. 1, the electronic network environment 100 may include aplurality of mobile devices 102, a plurality of content servers 104, aplurality of nodes 106, an infrastructure network 108, a networkanalytics server 110, and a predictive prefetch server 112. For ease ofexplanation, only a limited number of network elements are shown in theelectronic network environment 100 depicted in FIG. 1. However, itshould be understood that a greater number of network elements may bepart of the electronic network environment 100, including other types ofknown network entities not illustrated in FIG. 1. Other embodiments mayinclude additional or different network entities in alternativeconfigurations than which are exemplified in FIG. 1. Additionally,embodiments described herein may be presented within the context of thewireless communication networks for ease of explanation. However,aspects of the present disclosure are not restricted to the wirelesscommunication networks.

The plurality of mobile devices 102 may communicate with infrastructurenetwork 108 through the plurality of nodes 106 (e.g., wireless networkantennas) via a wireless connection. The infrastructure network 108 maybe used to transmit data between the plurality of mobile devices 102 andthe plurality of content servers 104 via one or more nodes 106.

Mobile devices 102 may include software applications with a variety offunctionalities. A software application running on mobile device 102 maylog the content consumption by a user of the mobile device 102. Thesoftware application may use the log of content consumed by the user todetermine a prefetch request or a set of prefetch requests to transmitto the predictive prefetch server 112. The software applications maytransmit prefetch requests for content, such web pages, videos, images,and files, for potential future access by a user of the mobile device102. For example, a user of mobile device may enter an address (e.g., aURL) of a desired web page and based on the log of content consumed bythe user, corresponding web pages may be requested to be prefetched forpotential future use by the user of mobile device 102. Once a prefetchrequest is fulfilled, the file for each corresponding web page may bestored in a memory of the mobile device 102 for instant access by theuser.

Each time an application transmits a content request, the applicationmay check the memory of the mobile device 102 to determine whether thecontent has been previously stored, and may provide the content fordisplay to the user from the memory when the content is present in thememory. For example, if the content has been previously stored in thememory, the application may retrieve the content from the memory of themobile device 102, which is relatively faster than retrieving thecontent from over the network.

The network analytics server 110 may communicate with network elementsthroughout the electronic network environment 100 to manage and monitordata transmitted between the devices. For example, the network analyticsserver 110 may receive traffic measurements and network element statusfrom the plurality of mobile devices 102, the plurality of nodes 106,and/or network elements within the infrastructure network 108. Basedupon the traffic measurements and/or the network element statusreceived, network analytics server 110 may measure the capacity of theinfrastructure network 108, and may provide the unused capacityinformation to the predictive prefetch server 112. The network analyticsserver 110 may transmit unused capacity information to predictiveprefetch server 112 at predetermined intervals (e.g., every second orfew seconds) to provide a real-time or near-real-time status of thenetwork environment 100.

Additionally, or alternatively, network analytics server 110 may measuretraffic data throughput for the entire network at a granular level. Inone embodiment of the present disclosure, raw traffic data may becollected across the plurality of mobile devices 102, the plurality ofnodes 106, and/or network elements within the infrastructure network 108to measure traffic flow. Raw traffic data from network analytics server110 may be transmitted to predictive prefetch server 112 atpredetermined intervals (e.g., every second or few seconds) to provide areal-time or near-real-time status of the network environment 100. Theraw traffic data may be analyzed by predictive prefetch server 112 todetermine an amount of unused capacity. A relatively high amount ofunused capacity in network environment 100 may allow for a relativelyhigher threshold value for fulfilling more prefetch requests. Thedynamic threshold value enables a wireless service provider for theplurality of mobile devices 102, the plurality of nodes 106, and/ornetwork elements within the infrastructure network 108 to utilizeotherwise unused network capacity.

As discussed above, the predictive prefetch server 112 may receive oneor more of unused capacity information and raw traffic data from networkanalytics server 110, and determine a threshold value based on one ormore of unused capacity information and raw traffic data. For example,the predictive prefetch server 112 may convert raw traffic data fromnetwork analytics server 110 into a quantifiable network capacitymeasure that may be used to determine unused network capacity. Thedetermined unused network capacity may then be used to fulfill prefetchrequests from one or more mobile devices 102. In one embodiment, networkanalytics server 110 and/or the predictive prefetch server 112 maycompare raw traffic data against a maximum network capacity to determinean unused network capacity.

Upon receiving the unused network capacity or determining the unusednetwork capacity, the predictive prefetch server 112 may determine athreshold value of a number of prefetch requests to fulfill. Forexample, when the averaged unused network capacity is about 90% of amaximum network capacity, the threshold value may be from about 0% toabout 90% of the prefetch request fulfillment. When the unused networkcapacity is about 10% of the maximum network capacity, the networkutilization may be about 0% to about 10%. Alternatively, a particularutilization threshold percentage (e.g., such as 70%, 80%, or 90%) of theunused network capacity may be used to fulfill prefetch request. Asmentioned above, the unused network capacity is a dynamic value, and thepredictive prefetch server may dynamically adjust the threshold valuebased on the received and/or determined unused network capacity.

One or more applications running on the mobile device 102 may alsotransmit prefetch requests to the predictive prefetch server 112. Thepredictive prefetch server 112 may then score each prefetch requestreceived from the mobile device 102 based on a likelihood that the userof the mobile device 102 may request the content of the prefetchrequest. The predictive prefetch server 112 may also review the prefetchrequests from mobile device 102 and determine a significance of thecontent of each request. For example, rapidly changing content, such asstatus updates and news feeds, may have a lower score and a lower rank,while static content, such as a news article or a video, may have ahigher score and a higher rank. Alternatively, rapidly changing content,such as status updates and news feeds, may have a higher score and ahigher rank, while static content, such as a news article or a video,may have a lower score and a lower rank

After scoring each prefetch request, the predictive prefetch server 112may rank the scored prefetch requests. Then, the predictive prefetchserver 112 may fulfill all or a percentage of the ranked prefetchrequests based on the threshold value. For example, the predictiveprefetch server 112 may fulfill the prefetch requests at or above thethreshold value. Additionally, or alternatively, the predictive prefetchserver 112 may respond to the prefetch requests based on the thresholdvalue and the score for the prefetch request. For example, if the scoreof the prefetch request is greater than or equal to the determinedthreshold value, then the one or more servers may respond to theprefetch request by fulfilling the prefetch requests. However, if thescore of the prefetch request is less than the determined thresholdvalue, then the one or more servers may not respond to the prefetchrequest.

Alternatively, or additionally, the predictive prefetch server 112 maytransmit instructions to a server of the network provider, such asnetwork analytics server 110 and/or a network element of infrastructurenetwork 108, to provide a response to prefetch requests based on thescore of each prefetch request and the threshold value.

The predictive prefetch server 112 may also score, rank, and fulfillprefetch requests based on a prefetch model built from user profiles.User activity with respect to content requests by the mobile device 102may be monitored in order to build a predictive model to determine whichprefetch requests to fulfill. The predictive prefetch server 112 maypredict which prefetch requests the user is likely to request in thefuture. The predictive prefetch server 112 may also determine othercontent that the user of mobile device 102 may be predicted to beinterested in receiving. The content is then prefetched and stored in alocal memory of a mobile device 102 of the user for later retrieval andconsumption by the user.

The predictive prefetch server 112 may analyze the content of a prefetchrequest and the content of the user requests from a mobile device 102 todetermine which prefetch requests the user will be likely to request inthe near future. Content may include any form of information accessibleto the mobile device 102 from content servers 104 over infrastructurenetwork 108, such as video, music, other audio files, websites, news,sports scores, and other forms of content. In addition, the predictiveprefetch server 112 may profile the different content categories of theprefetch requests and requests of the user of the mobile device 102.These predictive prefetch models may be used to determine whichresponses to prefetch requests may be transmitted to the mobile device102. The predictive prefetch server 112 may transmit the responses tothe prefetch requests based on the threshold value determined from theunused network capacity and the predictive prefetch model.

Using the various user profiles and user-consumed content stored in apredictive prefetch database 112 a, the predictive prefetch server 112may build a prefetch model unique to each user of a mobile device 102.Additionally, or alternatively, the predictive prefetch server 112 mayuse user profiles and consumption patterns of other users to build aprefetch model unique to each user of a mobile device 102. Thepredictive prefetch model may be used to determine a score and a rankingof each prefetch request received from a mobile device 102.Additionally, or alternative, the predictive prefetch model may be usedto predict whether a user will request content in the future, andtransmit predicted content to mobile device 102.

When the predictive prefetch server 112 determines content to prefetchfor the mobile device 102, the predictive prefetch server 112 may cachea response to the prefetch request in the user database 112 a, andfulfill the prefetch request when the threshold value determined fromthe unused network capacity is at an acceptable level.

In some embodiments, the predictive prefetch server 112 may prefetchcontent that it recommends to a user of a mobile device 102 based on aprefetch model built from the profile of the user and past contentrequests. The recommended prefetch content may be transmitted to themobile device 102 when the predictive prefetch server 112 determinesthat the threshold value of the unused network capacity is at anacceptable level.

The predictive prefetch server 112 may compile one or more profiles foreach user of mobile devices 102 based on past behavior that modelsinterest by the user in content (e.g., categories, genres, contenttypes, etc.). Similarly, the predictive prefetch server 112 may buildprofiles for different content types in terms of their refresh rate(e.g., news ticker has a faster refresh rate than music videos).

As shown in FIG. 2, the predictive prefetch server 112 may build and/orupdate a prefetch model for a user of a mobile device 102 from amultitude of sources, such as, content requests from each mobile device102, prefetch requests from each mobile device 102, user activityhistory stored in the predictive prefetch database 112 a, a prefetchmodel of the user of mobile device 102 stored in the predictive prefetchdatabase 112 a, and prefetch models of other users of mobile devicesstored in the predictive prefetch database 112 a. User activity historymay be a collection of information gathered about the user from previousprefetch requests and content requests from the mobile device 102 of theuser, and include, for example, preferences, interactions, activities,locations, etc. of the user of mobile device 102. The predictiveprefetch server 112 may also build the predictive prefetch model byreviewing the content requests and prefetch requests from mobile device102 and determine the significance of the content of each request. Forexample, rapidly changing content, such as status updates and newsfeeds, may have a lower score and a lower rank, while static content,such as a news article or a video, may have a higher score and a higherrank.

The predictive prefetch server 112 may then optimize which prefetchrequest to fulfill and detect fraudulent prefetch requests received frommobile device 102. As mentioned above, the predictive prefetch server112 may build the predictive prefetch models from interactive userinput, user preferences, content preferences, and activity times. Inthis regard, the predictive prefetch server 112 may use the predictiveprefetch model to determine whether prefetch requests received from themobile device 102 of the user deviates from the predictive prefetchmodel of the user.

FIG. 3 depicts a block diagram of a method for utilizing unused networkcapacity for prefetch requests, according to embodiments of the presentdisclosure. The method 300 may begin at step 302 in which one or moreservers, such as predictive prefetch server 112, may receive, via anetwork, network traffic information from a network provider. Thenetwork traffic information may include one or more of unused capacityinformation, raw traffic data, and/or a maximum network capacity.

The method may then proceed to step 304, in which the one or moreservers may determine a threshold value for prefetch requests to fulfillbased on the network traffic information. For example, the one or moreservers may determine the threshold value may be a percentage of, orall, of the unused network capacity. As mentioned above, the unusednetwork capacity may be a dynamic threshold value, and the one or moreservers may dynamically determine the threshold value based on thereceived network traffic information.

Then, at step 306, the one or more servers, may receive a plurality ofprefetch requests from an application running on a mobile deviceconnected to the network of the network provider. Each prefetch requestmay include a request for content predicted to be requested by the userof the mobile device. Alternatively, or additionally, the one or moreservers may receive the plurality of prefetch requests from anapplication server that transmits and receives data from the applicationrunning on the mobile device connected to the network of the networkprovider.

At step 308, the one or more servers may determine a score for eachprefetch request based on the received plurality of prefetch requestsand/or a plurality of prior prefetch requests. Finally, at step 310, theone or more servers may respond to the prefetch requests based on thethreshold value and the score for the prefetch request. For example, ifthe score of the prefetch request is greater than or equal to thedetermined threshold value, then the one or more servers may respond tothe prefetch request by fulfilling the prefetch requests. However, ifthe score of the prefetch request is less than the determined thresholdvalue, then the one or more servers may not respond to the prefetchrequest. Alternatively, or additionally, the one or more servers maytransmit instructions to a server of the network provider, such asnetwork analytics server 110 or a network element of infrastructurenetwork 108, to provide a response to prefetch requests based on thescore of each prefetch requests and the threshold value.

FIG. 4 depicts a block diagram of another method for utilizing unusednetwork capacity for prefetch requests, according to embodiments of thepresent disclosure. The method 400 may begin at step 402 in which one ormore servers, such as predictive prefetch server 112, may receive, via anetwork, network traffic information from a network provider. Thenetwork traffic information may include raw traffic data and a maximumnetwork capacity.

The method may then proceed to step 404, in which the one or moreservers determines an unused network capacity for the network of thenetwork provider based on the received network traffic information fromthe network provider. For example, the one or more servers may convertraw traffic data into a quantifiable network capacity measure. The oneor more servers may then determine the unused network capacity bycomparing the quantifiable network capacity measure to the maximumnetwork capacity.

The method may then proceed to step 406, in which the one or moreservers may determine a threshold value of prefetch requests to fulfillbased on the determined unused network capacity. For example, the one ormore servers may determine the threshold value may be a percentage of,or all, of the unused network capacity. As mentioned above, the unusednetwork capacity may be a dynamic threshold value, and the one or moreservers may dynamically determine the threshold value based on thereceived network traffic information.

Then, at step 408, the one or more servers may receive a plurality ofprefetch requests from an application running on a mobile deviceconnected to the network of the network provider. Each prefetch requestmay include a request for content predicted to be requested by the userof the mobile device. Alternatively, or additionally, the one or moreservers may receive the plurality of prefetch requests from anapplication server that transmits and receives data from the applicationrunning on the mobile device connected to the network of the networkprovider. At step 410, the one or more servers may determine a score ofeach prefetch request based on the plurality of prefetch requests.

At step 412, the one or more servers may respond to the prefetchrequests based on the threshold value and the score for each prefetchrequest. For example, if the score of each prefetch request is greaterthan or equal to the determined threshold value, then the one or moreservers may respond to the prefetch request by fulfilling the prefetchrequests. However, if the score of each prefetch request is less thanthe determined threshold value, then the one or more servers may notrespond to the prefetch request. Alternatively, or additionally, the oneor more servers may transmit instructions to a server of the networkprovider, such as network analytics server 110 or a network element ofinfrastructure network 108, to provide a response to prefetch requestsbased on the score of each prefetch request and the threshold value.

FIG. 5 depicts a block diagram of method for building a predictive modelfor prefetch requests, according to embodiments of the presentdisclosure. The method 500 may begin at step 502 in which one or moreservers, such as predictive prefetch server 112, may receive, via anetwork, a plurality of prefetch requests from an application running ona mobile device connected to the network of a network provider. Eachprefetch request may include a request for content predicted to berequested by the user of the mobile device.

At step 504, the one or more servers may receive, via the network, aplurality of content requests from a user of the application running onthe mobile device. Each content request may include a request forcontent made by the user in a non-prefetch capacity.

The method may then proceed to step 506, in which the one or moreservers may build a prefetch model for the user of the mobile devicebased on the received plurality of content requests and the receivedplurality of prefetch requests. Additionally, or alternatively, the oneor more servers may build the prefetch model for the user based on useractivity history stored in a database, such as predictive prefetchdatabase 112 a, a previously stored prefetch model of the user of mobiledevice stored in the database, and/or one or more prefetch models ofother users of mobile devices stored in the database. The one or moreservers may build the prefetch model by reviewing the content requestsand prefetch requests from mobile device. Then, at step 508, the one ormore servers may store the built prefetch model for the user in adatabase, such as predictive prefetch database 112 a.

At step 510, the one or more servers may compare each of the receivedplurality of prefetch requests to the built prefetch model for the user.For example, the one or more servers may analyze the content of aprefetch request, and compare the content of the prefetch request to theprefetch model. When the prefetch request or the content for theprefetch request matches the prefetch model, the one or more servers maydetermine that the prefetch requests is a proper prefetch request, e.g.,the prefetch request is non-fraudulent.

FIG. 6 depicts a block diagram of a method for utilizing unused networkcapacity for fulfilling prefetch requests using a predictive model,according to embodiments of the present disclosure. The method 600 maybegin at step 602 in which one or more servers, such as predictiveprefetch server 112, may receive, via a network, network trafficinformation from a network provider. The network traffic information mayinclude one or more of unused capacity information, raw traffic data,and/or a maximum network capacity.

The method may then proceed to step 604, in which the one or moreservers may determine a threshold value for prefetch request to fulfillbased on the network traffic information. For example, the one or moreservers may determine the threshold value may be a percentage of, orall, of the unused network capacity. As mentioned above, the unusednetwork capacity may be a dynamic threshold value, and the one or moreservers may dynamically determine the threshold value based on thereceived network traffic information.

Then, at step 606, the one or more servers may receive, via the network,a plurality of prefetch requests from an application running on a mobiledevice connected to the network of the network provider. Each prefetchrequest may include a request for content predicted to be requested bythe user of the mobile device.

At step 608, the one or more servers may receive, via the network, aplurality of content requests from a user of the application running onthe mobile device. Each content request may include a request forcontent made by the user in a non-prefetch capacity.

The method may then proceed to step 610, in which the one or moreservers may build a prefetch model for the user of the mobile devicebased on the received plurality of content requests and the receivedplurality of prefetch requests. Additionally, or alternatively, the oneor more servers may build the prefetch model for the user based on useractivity history stored in a database, such as predictive prefetchdatabase 112 a, a previously stored prefetch model of the user of mobiledevice stored in the database, and/or one or more prefetch models ofother users of mobile devices stored in the database.

At step 612, the one or more servers may compare each of the receivedplurality of prefetch requests to the built prefetch model for the user.For example, the one or more servers may analyze the content of aprefetch request, and compare the content of the prefetch request to theprefetch model. When the prefetch request or the content for theprefetch request matches the prefetch model, the one or more servers maydetermine that the prefetch request is a proper prefetch request, e.g.,the prefetch request is non-fraudulent.

Then at step 614, the one or more servers, for each prefetch requestthat matches the prefetch model, may determine a score for the prefetchrequest based on the plurality of prefetch request and the prefetchmodel. For example, prefetch request for rapidly changing content, suchas status updates and news feeds, may have a lower score and a lowerrank, while static content, such as a news article or a video, may havea higher score and a higher rank.

Finally, at step 616, the one or more servers may respond to theprefetch requests based on the threshold value and the score for theprefetch request. For example, if the score of the prefetch request isgreater than or equal to the determined threshold value, then the one ormore servers may respond to the prefetch request by fulfilling theprefetch requests. For example, the response to the prefetch request mayinclude the content requested by the prefetch request. The mobile devicemay store the response to the prefetch request in a memory of the mobiledevice, and when a user requests the content of the prefetch requests,the application may retrieve the content from the memory of the mobiledevice, which is relatively faster than retrieving the content from overthe network of the network provider. However, if the score of theprefetch request is less than the determined threshold value, then theone or more servers may not respond to the prefetch request.Alternatively, or additionally, the one or more servers may transmitinstructions to a server of the network provider, such as networkanalytics server 110 or a network element of infrastructure network 108,to provide a response to prefetch requests based on the score of eachprefetch request and the threshold value.

FIG. 7 is a simplified functional block diagram of a computer that maybe configured as the mobile devices, servers, nodes and/or networkelements for executing the methods, according to exemplary an embodimentof the present disclosure. Specifically, in one embodiment, any of theuser devices, servers, and/or exchanges may be an assembly of hardware700 including, for example, a data communication interface 760 forpacket data communication. The platform may also include a centralprocessing unit (“CPU”) 720, in the form of one or more processors, forexecuting program instructions. The platform typically includes aninternal communication bus 710, program storage, and data storage forvarious data files to be processed and/or communicated by the platformsuch as ROM 730 and RAM 740, although the system 700 often receivesprogramming and data via network communications. The system 700 also mayinclude input and output ports 750 to connect with input and outputdevices such as keyboards, mice, touchscreens, monitors, displays, etc.Of course, the various system functions may be implemented in adistributed fashion on a number of similar platforms, to distribute theprocessing load. Alternatively, the systems may be implemented byappropriate programming of one computer hardware platform.

Program aspects of the technology may be thought of as “products” or“articles of manufacture” typically in the form of executable codeand/or associated data that is carried on or embodied in a type ofmachine-readable medium. “Storage” type media include any or all of thetangible memory of the computers, processors or the like, or associatedmodules thereof, such as various semiconductor memories, tape drives,disk drives and the like, which may provide non-transitory storage atany time for the software programming. All or portions of the softwaremay at times be communicated through the Internet or various othertelecommunication networks. Such communications, for example, may enableloading of the software from one computer or processor into another, forexample, from a management server or host computer of the mobilecommunication network into the computer platform of a server and/or froma server to the mobile device. Thus, another type of media that may bearthe software elements includes optical, electrical and electromagneticwaves, such as used across physical interfaces between local devices,through wired and optical landline networks and over various air-links.The physical elements that carry such waves, such as wired or wirelesslinks, optical links, or the like, also may be considered as mediabearing the software. As used herein, unless restricted tonon-transitory, tangible “storage” media, terms such as computer ormachine “readable medium” refer to any medium that participates inproviding instructions to a processor for execution.

While the presently disclosed methods, devices, and systems aredescribed with exemplary reference to transmitting data, it should beappreciated that the presently disclosed embodiments may be applicableto any environment, such as a desktop or laptop computer, an automobileentertainment system, a home entertainment system, etc. Also, thepresently disclosed embodiments may be applicable to any type ofInternet protocol.

As will be recognized, the present disclosure is not limited to theseparticular embodiments. For instance, although described in the contextof utilizing unused network capacity for prefetch requests, the presentdisclosure may also utilized unused network capacity for other requests.

Other embodiments of the disclosure will be apparent to those skilled inthe art from consideration of the specification and practice of thedisclosure disclosed herein. It is intended that the specification andexamples be considered as exemplary only, with a true scope and spiritof the disclosure being indicated by the following claims.

What is claimed is:
 1. A computer-implemented method for utilizingunused network capacity for prefetch requests, the method comprising:receiving, over a network at one or more servers, network trafficinformation from a network provider of the network; determining, overthe network at the one or more servers, a plurality of prefetch requestsassociated with a device; determining, for each prefetch request of theplurality of prefetch requests by the one or more servers, a rankingbased on a prediction of which of the plurality of prefetch requests thedevice is most likely to use; and responding to, for each prefetchrequest of the plurality of prefetch requests by the one or moreservers, the prefetch request based on the network traffic informationand the ranking for the prefetch request.
 2. The method of claim 1,wherein responding to the prefetch request based on the network trafficinformation further comprises: determining, by the one or more servers,an unused network capacity for the network based on the network trafficinformation; and determining a threshold value based on the unusednetwork capacity.
 3. The method of claim 2, wherein the threshold valueis a percentage of the unused network capacity.
 4. The method of claim1, wherein determining the ranking for the prefetch request based on theplurality of prefetch requests is further based on at least one of alikelihood that a user of the device requests content of the prefetchrequest, and content of the prefetch request.
 5. The method of claim 1,further comprising: receiving, over the network by the one or moreservers, a plurality of content requests from a user of an applicationrunning on the device; and budding, by the one or more servers, aprefetch model for the user of the device based on the plurality ofcontent requests and the plurality of prefetch requests.
 6. The methodof claim 5, further comprising: comparing, for each prefetch request ofthe plurality of prefetch requests by the one or more servers, theprefetch request to the prefetch model, wherein determining the rankingfor the prefetch request based on the plurality of prefetch requestsincludes: determining, for each prefetch request of the plurality ofprefetch requests by the one or more servers, a score for the prefetchrequest based on the plurality of prefetch requests and the prefetchmodel when the prefetch request matches the prefetch model.
 7. Themethod of claim 2, wherein responding to the prefetch request based onthe network traffic information further comprises: transmitting, overthe network by the one or more servers, instructions to a server of thenetwork provider to provide a response to prefetch request based on thethreshold value and the ranking for the prefetch request.
 8. A systemfor utilizing unused network capacity for prefetch requests, the systemincluding: at least one data storage device that stores instructions forutilizing unused network capacity for prefetch requests; and at leastone processor configured to execute the instructions to performoperations comprising: receiving, over a network, network trafficinformation from a network provider of the network; determining, overthe network, a plurality of prefetch requests associated with a device;determining, for each prefetch request of the plurality of prefetchrequests by the one or more servers, a ranking based on a prediction ofwhich of the plurality of prefetch requests the device is most likely touse; and responding to, for each prefetch request of the plurality ofprefetch requests by the one or more servers, the prefetch request basedon the network traffic information and the ranking for the prefetchrequest.
 9. The system of claim 8, wherein responding to the prefetchrequest based on the network traffic information further comprises:determining, by the one or more servers, an unused network capacity forthe network based on the network traffic information; and determining athreshold value based on the unused network capacity.
 10. The system ofclaim 9, wherein the threshold value is a percentage of the unusednetwork capacity.
 11. The system of claim 8, wherein determining theranking for the prefetch request based on the plurality of prefetchrequests is further based on at least one of a likelihood that a user ofthe device requests content of the prefetch request, and content of theprefetch request.
 12. The system of claim 8, wherein the operationsfurther include: receiving, over the network, a plurality of contentrequests from a user of an application running on the device; andbudding a prefetch model for the user of the device based on thereceived plurality of content requests and the plurality of prefetchrequests.
 13. The system of claim 12, wherein the operations furtherinclude: comparing, for each prefetch request of the plurality ofprefetch requests, the prefetch request to the prefetch model, whereindetermining the ranking for the prefetch request based on the pluralityof prefetch requests includes: determining, for each prefetch request ofthe plurality of prefetch requests, a score for the prefetch requestbased on the plurality of prefetch requests and the prefetch model whenthe prefetch request matches the prefetch model.
 14. The system of claim9, wherein responding to the prefetch request based on the networktraffic information further comprises: transmitting, over the network,instructions to a server of the network provider to provide a responseto prefetch request based on the threshold value and the ranking for theprefetch request.
 15. A non-transitory computer-readable medium storinginstructions that, when executed by a computer, cause the computer toperform operations for utilizing unused network capacity for prefetchrequests, the operations comprising: receiving, over a network, networktraffic information from a network provider of the network; determining,over the network, a plurality of prefetch requests associated with adevice; determining, for each prefetch request of the plurality ofprefetch requests by the one or more servers, a ranking based on aprediction of which of the plurality of prefetch requests the device ismost likely to use; and responding to, for each prefetch request of theplurality of prefetch requests by the one or more servers, the prefetchrequest based on the network traffic information and the ranking for theprefetch request.
 16. The computer-readable medium of claim 15, whereinresponding to the prefetch request based on the network trafficinformation further comprises: determining, by the one or more servers,an unused network capacity for the network based on the network trafficinformation; and determining a threshold value based on the unusednetwork capacity.
 17. The computer-readable medium of claim 16, whereinthe threshold value is a percentage of the unused network capacity. 18.The computer-readable medium of claim 15, wherein determining theranking for the prefetch request based on the plurality of prefetchrequests is further based on at least one of a likelihood that a user ofthe device requests content of the prefetch request, and content of theprefetch request.
 19. The computer-readable medium of claim 15, whereinthe operations further comprise: receiving, over the network, aplurality of content requests from a user of an application running onthe device; and building a prefetch model for the user of the devicebased on the received plurality of content requests and the plurality ofprefetch requests.
 20. The computer-readable medium of claim 19, whereinthe operations further comprise: comparing, for each prefetch request ofthe plurality of prefetch requests, the prefetch request to the prefetchmodel, wherein determining the ranking for the prefetch request based onthe plurality of prefetch requests includes: determining, for eachprefetch request of the plurality of prefetch requests, a score for theprefetch request based on the plurality of prefetch requests and theprefetch model when the prefetch request matches the prefetch model.